A Tutorial on GeoAI: Designing Footprint Extraction from NAIP Imagery Using U-Net, Grounding DINO, SAM, and Mask R-CNN
Building footprint extraction from aerial imagery is a long-standing challenge in geospatial analysis, with applications ranging from urban planning to disaster response. A newly published tutorial from MarkTechPost lays out a full end-to-end pipeline using four models - U-Net, Grounding DINO, SAM (Segment Anything Model), and Mask R-CNN - applied to NAIP (National Agriculture Imagery Program) imagery, which provides high-resolution, four-band aerial coverage across the United States.
The workflow begins with environment configuration for geospatial deep learning, followed by the download of raster imagery and vector label data. Before any model training takes place, the tutorial emphasizes inspecting the spatial properties of both inputs - ensuring coordinate reference systems align and that chip generation produces georeferenced tiles suitable for training. This preprocessing step is often where real-world geospatial projects encounter friction, and the tutorial treats it as a first-class concern rather than an afterthought.
On the modeling side, the tutorial trains a U-Net with a ResNet-34 backbone as the supervised segmentation baseline, a well-established pairing in remote sensing tasks. It then introduces Grounding DINO as a text-prompted object detector and SAM as a prompt-based segmentation model - both of which have seen significant adoption since their respective releases for zero-shot and few-shot spatial tasks. Mask R-CNN provides an instance segmentation comparison, allowing readers to evaluate how different architectural philosophies handle the same extraction problem. Taken together, the four models represent a spectrum from fully supervised to prompt-driven inference.
The tutorial's value is in bridging the gap between general computer vision tooling and the specific demands of geospatial data, where projections, pixel resolution, and spatial metadata matter as much as model architecture. For practitioners working with satellite or aerial imagery, it provides a concrete template that can be adapted to other overhead datasets or extraction targets beyond building footprints.
